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Funari A, Fiscon G, Paci P. Network medicine and systems pharmacology approaches to predicting adverse drug effects. Br J Pharmacol 2024. [PMID: 39262113 DOI: 10.1111/bph.17330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 09/13/2024] Open
Abstract
Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses.
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Affiliation(s)
- Alessio Funari
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
- Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Rome, Italy
| | - Paola Paci
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
- Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Rome, Italy
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Firoozbakht F, Elkjaer ML, Handy D, Wang R, Chervontseva Z, Rarey M, Loscalzo J, Baumbach J, Tsoy O. DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.20.602911. [PMID: 39091742 PMCID: PMC11291051 DOI: 10.1101/2024.07.20.602911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes.
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Menichetti G, Barabási AL, Loscalzo J. Decoding the Foodome: Molecular Networks Connecting Diet and Health. Annu Rev Nutr 2024; 44:257-288. [PMID: 39207880 DOI: 10.1146/annurev-nutr-062322-030557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Diet, a modifiable risk factor, plays a pivotal role in most diseases, from cardiovascular disease to type 2 diabetes mellitus, cancer, and obesity. However, our understanding of the mechanistic role of the chemical compounds found in food remains incomplete. In this review, we explore the "dark matter" of nutrition, going beyond the macro- and micronutrients documented by national databases to unveil the exceptional chemical diversity of food composition. We also discuss the need to explore the impact of each compound in the presence of associated chemicals and relevant food sources and describe the tools that will allow us to do so. Finally, we discuss the role of network medicine in understanding the mechanism of action of each food molecule. Overall, we illustrate the important role of network science and artificial intelligence in our ability to reveal nutrition's multifaceted role in health and disease.
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Affiliation(s)
- Giulia Menichetti
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA;
- Network Science Institute and Department of Physics, Northeastern University, Boston, Massachusetts, USA
- Harvard Data Science Initiative, Harvard University, Boston, Massachusetts, USA
| | - Albert-László Barabási
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA;
- Network Science Institute and Department of Physics, Northeastern University, Boston, Massachusetts, USA
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA;
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Liu W, Zhang J, Qiao G, Bian J, Dong B, Li Y. HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies. BMC Bioinformatics 2024; 25:196. [PMID: 38769492 DOI: 10.1186/s12859-024-05806-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 05/08/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects. RESULTS To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features. CONCLUSIONS HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error and area under receiver operating characteristic curve, and shows remarkable performance in cold-start scenarios.
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Affiliation(s)
- Wuyong Liu
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150006, China
| | - Jingyu Zhang
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China
| | - Guanyu Qiao
- Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Jilong Bian
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150006, China
| | - Benzhi Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150006, China
| | - Yang Li
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150006, China.
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5
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Abstract
The concept of drug repurposing is focused on the repositioning of drug molecules that have already undergone safety trials. There are different strategies for drug repurposing. Network-based strategy focuses on the evaluation of drug combinations in a molecular environment with multi-target hits and analysis of drug interactions. Implementation of any in silico strategy requires several databases and pipelines for executing the process of shortlisting appropriate drugs.
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Affiliation(s)
- Arjun V Kowshik
- Department of Biotechnology, PES University, Bengaluru, India
| | - Megha Manoj
- Department of Biotechnology, PES University, Bengaluru, India
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Petti M, Farina L. Network medicine for patients' stratification: From single-layer to multi-omics. WIREs Mech Dis 2023; 15:e1623. [PMID: 37323106 DOI: 10.1002/wsbm.1623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/08/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Precision medicine research increasingly relies on the integrated analysis of multiple types of omics. In the era of big data, the large availability of different health-related information represents a great, but at the same time untapped, chance with a potentially fundamental role in the prevention, diagnosis and prognosis of diseases. Computational methods are needed to combine this data to create a comprehensive view of a given disease. Network science can model biomedical data in terms of relationships among molecular players of different nature and has been successfully proposed as a new paradigm for studying human diseases. Patient stratification is an open challenge aimed at identifying subtypes with different disease manifestations, severity, and expected survival time. Several stratification approaches based on high-throughput gene expression measurements have been successfully applied. However, few attempts have been proposed to exploit the integration of various genotypic and phenotypic data to discover novel sub-types or improve the detection of known groupings. This article is categorized under: Cancer > Biomedical Engineering Cancer > Computational Models Cancer > Genetics/Genomics/Epigenetics.
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Affiliation(s)
- Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
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Wang RS, Loscalzo J. Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations. Circ Res 2023; 132:1374-1386. [PMID: 37167362 PMCID: PMC10171294 DOI: 10.1161/circresaha.122.321879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.
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Affiliation(s)
- Rui-Sheng Wang
- Channing Division of Network Medicine (R.-S.W., J.L.), Department of Medicine, Brigham and Women's Hospital, Harvard Medical School Boston, MA
| | - Joseph Loscalzo
- Channing Division of Network Medicine (R.-S.W., J.L.), Department of Medicine, Brigham and Women's Hospital, Harvard Medical School Boston, MA
- Division of Cardiovascular Medicine (J.L.), Department of Medicine, Brigham and Women's Hospital, Harvard Medical School Boston, MA
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Pandey AK, Loscalzo J. Network medicine: an approach to complex kidney disease phenotypes. Nat Rev Nephrol 2023:10.1038/s41581-023-00705-0. [PMID: 37041415 DOI: 10.1038/s41581-023-00705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.
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Affiliation(s)
- Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
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Recanatini M, Menestrina L. Network modeling helps to tackle the complexity of drug-disease systems. WIREs Mech Dis 2023:e1607. [PMID: 36958762 DOI: 10.1002/wsbm.1607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/03/2023] [Accepted: 03/03/2023] [Indexed: 03/25/2023]
Abstract
From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.
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Affiliation(s)
- Maurizio Recanatini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
| | - Luca Menestrina
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, Bologna, 40126, Italy
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Loscalzo J. Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning. FASEB J 2023; 37:e22660. [PMID: 36468661 PMCID: PMC10107166 DOI: 10.1096/fj.202201683r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 10/27/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022]
Abstract
Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.
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Affiliation(s)
- Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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11
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Lal JC, Mao C, Zhou Y, Gore-Panter SR, Rennison JH, Lovano BS, Castel L, Shin J, Gillinov AM, Smith JD, Barnard J, Van Wagoner DR, Luo Y, Cheng F, Chung MK. Transcriptomics-based network medicine approach identifies metformin as a repurposable drug for atrial fibrillation. Cell Rep Med 2022; 3:100749. [PMID: 36223777 PMCID: PMC9588904 DOI: 10.1016/j.xcrm.2022.100749] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/25/2022] [Accepted: 08/26/2022] [Indexed: 11/24/2022]
Abstract
Effective drugs for atrial fibrillation (AF) are lacking, resulting in significant morbidity and mortality. This study demonstrates that network proximity analysis of differentially expressed genes from atrial tissue to drug targets can help prioritize repurposed drugs for AF. Using enrichment analysis of drug-gene signatures and functional testing in human inducible pluripotent stem cell (iPSC)-derived atrial-like cardiomyocytes, we identify metformin as a top repurposed drug candidate for AF. Using the active compactor, a new design analysis of large-scale longitudinal electronic health record (EHR) data, we determine that metformin use is significantly associated with a reduced risk of AF (odds ratio = 0.48, 95%, confidence interval [CI] 0.36-0.64, p < 0.001) compared with standard treatments for diabetes. This study utilizes network medicine methodologies to identify repurposed drugs for AF treatment and identifies metformin as a candidate drug.
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Affiliation(s)
- Jessica C. Lal
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave., NE5-305, Cleveland, OH 44195, USA,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Chengsheng Mao
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave., NE5-305, Cleveland, OH 44195, USA
| | - Shamone R. Gore-Panter
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA,Department of Biological, Geological, and Environmental Sciences, Cleveland State University, Cleveland, OH 44115, USA
| | - Julie H. Rennison
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Beth S. Lovano
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Laurie Castel
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jiyoung Shin
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - A. Marc Gillinov
- Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jonathan D. Smith
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA,Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - John Barnard
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - David R. Van Wagoner
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA,Corresponding author
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave., NE5-305, Cleveland, OH 44195, USA,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA,Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA,Corresponding author
| | - Mina K. Chung
- Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA,Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, 9500 Euclid Ave., J2-2, OH 44195, USA,Corresponding author
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12
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Repurposing Histaminergic Drugs in Multiple Sclerosis. Int J Mol Sci 2022; 23:ijms23116347. [PMID: 35683024 PMCID: PMC9181091 DOI: 10.3390/ijms23116347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 05/31/2022] [Accepted: 06/03/2022] [Indexed: 11/16/2022] Open
Abstract
Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1–H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.
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